Overview

Dataset statistics

Number of variables35
Number of observations147
Missing cells147
Missing cells (%)2.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory45.4 KiB
Average record size in memory316.4 B

Variable types

Numeric15
Categorical17
Boolean2
Unsupported1

Alerts

EmployeeCount has constant value "1.0"Constant
Over18 has constant value "True"Constant
StandardHours has constant value "80.0"Constant
Age is highly overall correlated with TotalWorkingYearsHigh correlation
MonthlyIncome is highly overall correlated with TotalWorkingYears and 1 other fieldsHigh correlation
PercentSalaryHike is highly overall correlated with PerformanceRatingHigh correlation
TotalWorkingYears is highly overall correlated with Age and 3 other fieldsHigh correlation
YearsAtCompany is highly overall correlated with TotalWorkingYears and 2 other fieldsHigh correlation
YearsInCurrentRole is highly overall correlated with YearsAtCompany and 1 other fieldsHigh correlation
YearsWithCurrManager is highly overall correlated with YearsAtCompany and 1 other fieldsHigh correlation
Department is highly overall correlated with EducationField and 1 other fieldsHigh correlation
EducationField is highly overall correlated with DepartmentHigh correlation
JobLevel is highly overall correlated with MonthlyIncome and 2 other fieldsHigh correlation
JobRole is highly overall correlated with Department and 1 other fieldsHigh correlation
MaritalStatus is highly overall correlated with StockOptionLevelHigh correlation
PerformanceRating is highly overall correlated with PercentSalaryHikeHigh correlation
StockOptionLevel is highly overall correlated with MaritalStatusHigh correlation
Attrition has 147 (100.0%) missing valuesMissing
EmployeeNumber is uniformly distributedUniform
EmployeeNumber has unique valuesUnique
MonthlyRate has unique valuesUnique
Attrition is an unsupported type, check if it needs cleaning or further analysisUnsupported
NumCompaniesWorked has 15 (10.2%) zerosZeros
TrainingTimesLastYear has 5 (3.4%) zerosZeros
YearsAtCompany has 3 (2.0%) zerosZeros
YearsInCurrentRole has 15 (10.2%) zerosZeros
YearsSinceLastPromotion has 56 (38.1%) zerosZeros
YearsWithCurrManager has 21 (14.3%) zerosZeros

Reproduction

Analysis started2023-07-15 16:10:29.273488
Analysis finished2023-07-15 16:11:38.070725
Duration1 minute and 8.8 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

EmployeeNumber
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct147
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100073
Minimum100000
Maximum100146
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-07-15T16:11:38.258713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum100000
5-th percentile100007.3
Q1100036.5
median100073
Q3100109.5
95-th percentile100138.7
Maximum100146
Range146
Interquartile range (IQR)73

Descriptive statistics

Standard deviation42.579338
Coefficient of variation (CV)0.00042548277
Kurtosis-1.2
Mean100073
Median Absolute Deviation (MAD)37
Skewness0
Sum14710731
Variance1813
MonotonicityStrictly increasing
2023-07-15T16:11:38.569344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000 1
 
0.7%
100110 1
 
0.7%
100094 1
 
0.7%
100095 1
 
0.7%
100096 1
 
0.7%
100097 1
 
0.7%
100098 1
 
0.7%
100099 1
 
0.7%
100100 1
 
0.7%
100101 1
 
0.7%
Other values (137) 137
93.2%
ValueCountFrequency (%)
100000 1
0.7%
100001 1
0.7%
100002 1
0.7%
100003 1
0.7%
100004 1
0.7%
100005 1
0.7%
100006 1
0.7%
100007 1
0.7%
100008 1
0.7%
100009 1
0.7%
ValueCountFrequency (%)
100146 1
0.7%
100145 1
0.7%
100144 1
0.7%
100143 1
0.7%
100142 1
0.7%
100141 1
0.7%
100140 1
0.7%
100139 1
0.7%
100138 1
0.7%
100137 1
0.7%

Age
Real number (ℝ)

Distinct38
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.619048
Minimum19
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-07-15T16:11:38.885903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile25.3
Q131
median36
Q343.5
95-th percentile54
Maximum60
Range41
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation8.7861452
Coefficient of variation (CV)0.23355576
Kurtosis-0.39070085
Mean37.619048
Median Absolute Deviation (MAD)6
Skewness0.49796963
Sum5530
Variance77.196347
MonotonicityNot monotonic
2023-07-15T16:11:39.313420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
35 11
 
7.5%
32 9
 
6.1%
37 8
 
5.4%
33 8
 
5.4%
31 8
 
5.4%
42 7
 
4.8%
38 6
 
4.1%
28 6
 
4.1%
36 6
 
4.1%
34 5
 
3.4%
Other values (28) 73
49.7%
ValueCountFrequency (%)
19 1
 
0.7%
21 1
 
0.7%
23 1
 
0.7%
24 2
 
1.4%
25 3
2.0%
26 3
2.0%
27 4
2.7%
28 6
4.1%
29 4
2.7%
30 5
3.4%
ValueCountFrequency (%)
60 1
 
0.7%
59 1
 
0.7%
57 1
 
0.7%
56 2
 
1.4%
55 1
 
0.7%
54 3
2.0%
53 3
2.0%
51 3
2.0%
50 2
 
1.4%
49 5
3.4%

BusinessTravel
Categorical

Distinct3
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
Travel_Rarely
106 
Travel_Frequently
27 
Non-Travel
14 

Length

Max length17
Median length13
Mean length13.44898
Min length10

Characters and Unicode

Total characters1977
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTravel_Rarely
2nd rowTravel_Rarely
3rd rowTravel_Rarely
4th rowNon-Travel
5th rowTravel_Rarely

Common Values

ValueCountFrequency (%)
Travel_Rarely 106
72.1%
Travel_Frequently 27
 
18.4%
Non-Travel 14
 
9.5%

Length

2023-07-15T16:11:39.782674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T16:11:40.268446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
travel_rarely 106
72.1%
travel_frequently 27
 
18.4%
non-travel 14
 
9.5%

Most occurring characters

ValueCountFrequency (%)
e 307
15.5%
r 280
14.2%
l 280
14.2%
a 253
12.8%
T 147
7.4%
v 147
7.4%
y 133
6.7%
_ 133
6.7%
R 106
 
5.4%
n 41
 
2.1%
Other values (7) 150
7.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1536
77.7%
Uppercase Letter 294
 
14.9%
Connector Punctuation 133
 
6.7%
Dash Punctuation 14
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 307
20.0%
r 280
18.2%
l 280
18.2%
a 253
16.5%
v 147
9.6%
y 133
8.7%
n 41
 
2.7%
q 27
 
1.8%
u 27
 
1.8%
t 27
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
T 147
50.0%
R 106
36.1%
F 27
 
9.2%
N 14
 
4.8%
Connector Punctuation
ValueCountFrequency (%)
_ 133
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1830
92.6%
Common 147
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 307
16.8%
r 280
15.3%
l 280
15.3%
a 253
13.8%
T 147
8.0%
v 147
8.0%
y 133
7.3%
R 106
 
5.8%
n 41
 
2.2%
F 27
 
1.5%
Other values (5) 109
 
6.0%
Common
ValueCountFrequency (%)
_ 133
90.5%
- 14
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1977
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 307
15.5%
r 280
14.2%
l 280
14.2%
a 253
12.8%
T 147
7.4%
v 147
7.4%
y 133
6.7%
_ 133
6.7%
R 106
 
5.4%
n 41
 
2.1%
Other values (7) 150
7.6%

DailyRate
Real number (ℝ)

Distinct135
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean806.11565
Minimum117
Maximum1499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-07-15T16:11:40.718805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum117
5-th percentile170.5
Q1479.5
median773
Q31163
95-th percentile1394.7
Maximum1499
Range1382
Interquartile range (IQR)683.5

Descriptive statistics

Standard deviation405.47619
Coefficient of variation (CV)0.50300002
Kurtosis-1.2614206
Mean806.11565
Median Absolute Deviation (MAD)365
Skewness-0.027433958
Sum118499
Variance164410.94
MonotonicityNot monotonic
2023-07-15T16:11:41.224729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1395 2
 
1.4%
1373 2
 
1.4%
303 2
 
1.4%
1003 2
 
1.4%
381 2
 
1.4%
810 2
 
1.4%
1147 2
 
1.4%
1302 2
 
1.4%
1111 2
 
1.4%
335 2
 
1.4%
Other values (125) 127
86.4%
ValueCountFrequency (%)
117 1
0.7%
119 1
0.7%
120 1
0.7%
124 1
0.7%
138 1
0.7%
142 1
0.7%
148 1
0.7%
163 1
0.7%
188 1
0.7%
202 1
0.7%
ValueCountFrequency (%)
1499 1
0.7%
1476 1
0.7%
1434 1
0.7%
1401 1
0.7%
1400 1
0.7%
1396 1
0.7%
1395 2
1.4%
1394 1
0.7%
1392 1
0.7%
1381 1
0.7%

Department
Categorical

Distinct3
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
Research & Development
101 
Sales
40 
Human Resources
 
6

Length

Max length22
Median length22
Mean length17.088435
Min length5

Characters and Unicode

Total characters2512
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowResearch & Development
2nd rowResearch & Development
3rd rowSales
4th rowSales
5th rowResearch & Development

Common Values

ValueCountFrequency (%)
Research & Development 101
68.7%
Sales 40
 
27.2%
Human Resources 6
 
4.1%

Length

2023-07-15T16:11:41.718987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T16:11:42.167094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
research 101
28.5%
101
28.5%
development 101
28.5%
sales 40
 
11.3%
human 6
 
1.7%
resources 6
 
1.7%

Most occurring characters

ValueCountFrequency (%)
e 557
22.2%
208
 
8.3%
s 153
 
6.1%
a 147
 
5.9%
l 141
 
5.6%
R 107
 
4.3%
r 107
 
4.3%
c 107
 
4.3%
n 107
 
4.3%
m 107
 
4.3%
Other values (10) 771
30.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1949
77.6%
Uppercase Letter 254
 
10.1%
Space Separator 208
 
8.3%
Other Punctuation 101
 
4.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 557
28.6%
s 153
 
7.9%
a 147
 
7.5%
l 141
 
7.2%
r 107
 
5.5%
c 107
 
5.5%
n 107
 
5.5%
m 107
 
5.5%
o 107
 
5.5%
p 101
 
5.2%
Other values (4) 315
16.2%
Uppercase Letter
ValueCountFrequency (%)
R 107
42.1%
D 101
39.8%
S 40
 
15.7%
H 6
 
2.4%
Space Separator
ValueCountFrequency (%)
208
100.0%
Other Punctuation
ValueCountFrequency (%)
& 101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2203
87.7%
Common 309
 
12.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 557
25.3%
s 153
 
6.9%
a 147
 
6.7%
l 141
 
6.4%
R 107
 
4.9%
r 107
 
4.9%
c 107
 
4.9%
n 107
 
4.9%
m 107
 
4.9%
o 107
 
4.9%
Other values (8) 563
25.6%
Common
ValueCountFrequency (%)
208
67.3%
& 101
32.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2512
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 557
22.2%
208
 
8.3%
s 153
 
6.1%
a 147
 
5.9%
l 141
 
5.6%
R 107
 
4.3%
r 107
 
4.3%
c 107
 
4.3%
n 107
 
4.3%
m 107
 
4.3%
Other values (10) 771
30.7%

DistanceFromHome
Real number (ℝ)

Distinct26
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.7619048
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-07-15T16:11:42.591775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12.5
median8
Q315
95-th percentile27
Maximum29
Range28
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation8.4125911
Coefficient of variation (CV)0.86177763
Kurtosis-0.49794123
Mean9.7619048
Median Absolute Deviation (MAD)6
Skewness0.89876131
Sum1435
Variance70.771689
MonotonicityNot monotonic
2023-07-15T16:11:42.879359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
2 23
15.6%
1 14
 
9.5%
8 11
 
7.5%
3 10
 
6.8%
10 10
 
6.8%
9 9
 
6.1%
7 8
 
5.4%
5 7
 
4.8%
27 6
 
4.1%
4 6
 
4.1%
Other values (16) 43
29.3%
ValueCountFrequency (%)
1 14
9.5%
2 23
15.6%
3 10
6.8%
4 6
 
4.1%
5 7
 
4.8%
6 5
 
3.4%
7 8
 
5.4%
8 11
7.5%
9 9
 
6.1%
10 10
6.8%
ValueCountFrequency (%)
29 1
 
0.7%
28 2
 
1.4%
27 6
4.1%
25 4
2.7%
24 6
4.1%
23 5
3.4%
22 2
 
1.4%
20 3
2.0%
19 1
 
0.7%
18 1
 
0.7%

Education
Categorical

Distinct5
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
3.0
65 
4.0
41 
2.0
21 
1.0
15 
5.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row3.0
3rd row1.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.0 65
44.2%
4.0 41
27.9%
2.0 21
 
14.3%
1.0 15
 
10.2%
5.0 5
 
3.4%

Length

2023-07-15T16:11:43.152957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T16:11:43.420521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0 65
44.2%
4.0 41
27.9%
2.0 21
 
14.3%
1.0 15
 
10.2%
5.0 5
 
3.4%

Most occurring characters

ValueCountFrequency (%)
. 147
33.3%
0 147
33.3%
3 65
14.7%
4 41
 
9.3%
2 21
 
4.8%
1 15
 
3.4%
5 5
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294
66.7%
Other Punctuation 147
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 147
50.0%
3 65
22.1%
4 41
 
13.9%
2 21
 
7.1%
1 15
 
5.1%
5 5
 
1.7%
Other Punctuation
ValueCountFrequency (%)
. 147
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 147
33.3%
0 147
33.3%
3 65
14.7%
4 41
 
9.3%
2 21
 
4.8%
1 15
 
3.4%
5 5
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 147
33.3%
0 147
33.3%
3 65
14.7%
4 41
 
9.3%
2 21
 
4.8%
1 15
 
3.4%
5 5
 
1.1%

EducationField
Categorical

Distinct6
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
Medical
56 
Life Sciences
55 
Marketing
14 
Technical Degree
11 
Other

Length

Max length16
Median length15
Mean length10.095238
Min length5

Characters and Unicode

Total characters1484
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedical
2nd rowLife Sciences
3rd rowMedical
4th rowMedical
5th rowOther

Common Values

ValueCountFrequency (%)
Medical 56
38.1%
Life Sciences 55
37.4%
Marketing 14
 
9.5%
Technical Degree 11
 
7.5%
Other 9
 
6.1%
Human Resources 2
 
1.4%

Length

2023-07-15T16:11:43.678038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T16:11:43.964722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
medical 56
26.0%
life 55
25.6%
sciences 55
25.6%
marketing 14
 
6.5%
technical 11
 
5.1%
degree 11
 
5.1%
other 9
 
4.2%
human 2
 
0.9%
resources 2
 
0.9%

Most occurring characters

ValueCountFrequency (%)
e 292
19.7%
i 191
12.9%
c 190
12.8%
a 83
 
5.6%
n 82
 
5.5%
M 70
 
4.7%
68
 
4.6%
l 67
 
4.5%
s 59
 
4.0%
d 56
 
3.8%
Other values (16) 326
22.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1201
80.9%
Uppercase Letter 215
 
14.5%
Space Separator 68
 
4.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 292
24.3%
i 191
15.9%
c 190
15.8%
a 83
 
6.9%
n 82
 
6.8%
l 67
 
5.6%
s 59
 
4.9%
d 56
 
4.7%
f 55
 
4.6%
r 36
 
3.0%
Other values (7) 90
 
7.5%
Uppercase Letter
ValueCountFrequency (%)
M 70
32.6%
L 55
25.6%
S 55
25.6%
T 11
 
5.1%
D 11
 
5.1%
O 9
 
4.2%
H 2
 
0.9%
R 2
 
0.9%
Space Separator
ValueCountFrequency (%)
68
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1416
95.4%
Common 68
 
4.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 292
20.6%
i 191
13.5%
c 190
13.4%
a 83
 
5.9%
n 82
 
5.8%
M 70
 
4.9%
l 67
 
4.7%
s 59
 
4.2%
d 56
 
4.0%
L 55
 
3.9%
Other values (15) 271
19.1%
Common
ValueCountFrequency (%)
68
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 292
19.7%
i 191
12.9%
c 190
12.8%
a 83
 
5.6%
n 82
 
5.5%
M 70
 
4.7%
68
 
4.6%
l 67
 
4.5%
s 59
 
4.0%
d 56
 
3.8%
Other values (16) 326
22.0%

EmployeeCount
Categorical

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
1.0
147 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 147
100.0%

Length

2023-07-15T16:11:44.230319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T16:11:44.470935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 147
100.0%

Most occurring characters

ValueCountFrequency (%)
1 147
33.3%
. 147
33.3%
0 147
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294
66.7%
Other Punctuation 147
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 147
50.0%
0 147
50.0%
Other Punctuation
ValueCountFrequency (%)
. 147
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 147
33.3%
. 147
33.3%
0 147
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 147
33.3%
. 147
33.3%
0 147
33.3%
Distinct4
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
3.0
46 
4.0
45 
1.0
32 
2.0
24 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row4.0
4th row1.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.0 46
31.3%
4.0 45
30.6%
1.0 32
21.8%
2.0 24
16.3%

Length

2023-07-15T16:11:44.670051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T16:11:44.937171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0 46
31.3%
4.0 45
30.6%
1.0 32
21.8%
2.0 24
16.3%

Most occurring characters

ValueCountFrequency (%)
. 147
33.3%
0 147
33.3%
3 46
 
10.4%
4 45
 
10.2%
1 32
 
7.3%
2 24
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294
66.7%
Other Punctuation 147
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 147
50.0%
3 46
 
15.6%
4 45
 
15.3%
1 32
 
10.9%
2 24
 
8.2%
Other Punctuation
ValueCountFrequency (%)
. 147
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 147
33.3%
0 147
33.3%
3 46
 
10.4%
4 45
 
10.2%
1 32
 
7.3%
2 24
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 147
33.3%
0 147
33.3%
3 46
 
10.4%
4 45
 
10.2%
1 32
 
7.3%
2 24
 
5.4%

Gender
Categorical

Distinct2
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
Male
86 
Female
61 

Length

Max length6
Median length4
Mean length4.829932
Min length4

Characters and Unicode

Total characters710
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 86
58.5%
Female 61
41.5%

Length

2023-07-15T16:11:45.180457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T16:11:45.452308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
male 86
58.5%
female 61
41.5%

Most occurring characters

ValueCountFrequency (%)
e 208
29.3%
a 147
20.7%
l 147
20.7%
M 86
12.1%
F 61
 
8.6%
m 61
 
8.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 563
79.3%
Uppercase Letter 147
 
20.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 208
36.9%
a 147
26.1%
l 147
26.1%
m 61
 
10.8%
Uppercase Letter
ValueCountFrequency (%)
M 86
58.5%
F 61
41.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 710
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 208
29.3%
a 147
20.7%
l 147
20.7%
M 86
12.1%
F 61
 
8.6%
m 61
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 710
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 208
29.3%
a 147
20.7%
l 147
20.7%
M 86
12.1%
F 61
 
8.6%
m 61
 
8.6%

HourlyRate
Real number (ℝ)

Distinct61
Distinct (%)41.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.238095
Minimum30
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-07-15T16:11:45.690753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile34
Q148.5
median64
Q380
95-th percentile95.7
Maximum99
Range69
Interquartile range (IQR)31.5

Descriptive statistics

Standard deviation19.640723
Coefficient of variation (CV)0.30574883
Kurtosis-1.1079386
Mean64.238095
Median Absolute Deviation (MAD)16
Skewness0.037297134
Sum9443
Variance385.75799
MonotonicityNot monotonic
2023-07-15T16:11:45.998417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 5
 
3.4%
83 5
 
3.4%
56 4
 
2.7%
35 4
 
2.7%
51 4
 
2.7%
48 4
 
2.7%
77 4
 
2.7%
42 4
 
2.7%
74 4
 
2.7%
98 3
 
2.0%
Other values (51) 106
72.1%
ValueCountFrequency (%)
30 2
1.4%
31 3
2.0%
32 1
 
0.7%
34 3
2.0%
35 4
2.7%
37 3
2.0%
38 2
1.4%
39 2
1.4%
40 1
 
0.7%
42 4
2.7%
ValueCountFrequency (%)
99 2
1.4%
98 3
2.0%
97 1
 
0.7%
96 2
1.4%
95 2
1.4%
94 1
 
0.7%
93 3
2.0%
92 3
2.0%
91 2
1.4%
89 3
2.0%

JobInvolvement
Categorical

Distinct4
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
3.0
89 
2.0
33 
1.0
13 
4.0
12 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 89
60.5%
2.0 33
 
22.4%
1.0 13
 
8.8%
4.0 12
 
8.2%

Length

2023-07-15T16:11:46.308049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T16:11:46.577226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0 89
60.5%
2.0 33
 
22.4%
1.0 13
 
8.8%
4.0 12
 
8.2%

Most occurring characters

ValueCountFrequency (%)
. 147
33.3%
0 147
33.3%
3 89
20.2%
2 33
 
7.5%
1 13
 
2.9%
4 12
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294
66.7%
Other Punctuation 147
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 147
50.0%
3 89
30.3%
2 33
 
11.2%
1 13
 
4.4%
4 12
 
4.1%
Other Punctuation
ValueCountFrequency (%)
. 147
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 147
33.3%
0 147
33.3%
3 89
20.2%
2 33
 
7.5%
1 13
 
2.9%
4 12
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 147
33.3%
0 147
33.3%
3 89
20.2%
2 33
 
7.5%
1 13
 
2.9%
4 12
 
2.7%

JobLevel
Categorical

Distinct5
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2.0
58 
1.0
50 
3.0
19 
4.0
13 
5.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row3.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.0 58
39.5%
1.0 50
34.0%
3.0 19
 
12.9%
4.0 13
 
8.8%
5.0 7
 
4.8%

Length

2023-07-15T16:11:46.809634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T16:11:47.092526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 58
39.5%
1.0 50
34.0%
3.0 19
 
12.9%
4.0 13
 
8.8%
5.0 7
 
4.8%

Most occurring characters

ValueCountFrequency (%)
. 147
33.3%
0 147
33.3%
2 58
 
13.2%
1 50
 
11.3%
3 19
 
4.3%
4 13
 
2.9%
5 7
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294
66.7%
Other Punctuation 147
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 147
50.0%
2 58
 
19.7%
1 50
 
17.0%
3 19
 
6.5%
4 13
 
4.4%
5 7
 
2.4%
Other Punctuation
ValueCountFrequency (%)
. 147
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 147
33.3%
0 147
33.3%
2 58
 
13.2%
1 50
 
11.3%
3 19
 
4.3%
4 13
 
2.9%
5 7
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 147
33.3%
0 147
33.3%
2 58
 
13.2%
1 50
 
11.3%
3 19
 
4.3%
4 13
 
2.9%
5 7
 
1.6%

JobRole
Categorical

Distinct9
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
Sales Executive
33 
Research Scientist
30 
Laboratory Technician
26 
Manufacturing Director
13 
Healthcare Representative
13 
Other values (4)
32 

Length

Max length25
Median length21
Mean length17.843537
Min length7

Characters and Unicode

Total characters2623
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowResearch Scientist
2nd rowResearch Scientist
3rd rowSales Executive
4th rowSales Executive
5th rowResearch Scientist

Common Values

ValueCountFrequency (%)
Sales Executive 33
22.4%
Research Scientist 30
20.4%
Laboratory Technician 26
17.7%
Manufacturing Director 13
 
8.8%
Healthcare Representative 13
 
8.8%
Manager 12
 
8.2%
Research Director 11
 
7.5%
Sales Representative 5
 
3.4%
Human Resources 4
 
2.7%

Length

2023-07-15T16:11:47.348813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T16:11:47.685544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
research 41
14.5%
sales 38
13.5%
executive 33
11.7%
scientist 30
10.6%
laboratory 26
9.2%
technician 26
9.2%
director 24
8.5%
representative 18
6.4%
manufacturing 13
 
4.6%
healthcare 13
 
4.6%
Other values (3) 20
7.1%

Most occurring characters

ValueCountFrequency (%)
e 384
14.6%
a 255
 
9.7%
c 210
 
8.0%
t 205
 
7.8%
r 201
 
7.7%
i 200
 
7.6%
n 142
 
5.4%
s 135
 
5.1%
135
 
5.1%
o 80
 
3.0%
Other values (19) 676
25.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2206
84.1%
Uppercase Letter 282
 
10.8%
Space Separator 135
 
5.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 384
17.4%
a 255
11.6%
c 210
9.5%
t 205
9.3%
r 201
9.1%
i 200
9.1%
n 142
 
6.4%
s 135
 
6.1%
o 80
 
3.6%
h 80
 
3.6%
Other values (10) 314
14.2%
Uppercase Letter
ValueCountFrequency (%)
S 68
24.1%
R 63
22.3%
E 33
11.7%
T 26
 
9.2%
L 26
 
9.2%
M 25
 
8.9%
D 24
 
8.5%
H 17
 
6.0%
Space Separator
ValueCountFrequency (%)
135
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2488
94.9%
Common 135
 
5.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 384
15.4%
a 255
10.2%
c 210
 
8.4%
t 205
 
8.2%
r 201
 
8.1%
i 200
 
8.0%
n 142
 
5.7%
s 135
 
5.4%
o 80
 
3.2%
h 80
 
3.2%
Other values (18) 596
24.0%
Common
ValueCountFrequency (%)
135
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2623
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 384
14.6%
a 255
 
9.7%
c 210
 
8.0%
t 205
 
7.8%
r 201
 
7.7%
i 200
 
7.6%
n 142
 
5.4%
s 135
 
5.1%
135
 
5.1%
o 80
 
3.0%
Other values (19) 676
25.8%

JobSatisfaction
Categorical

Distinct4
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
4.0
51 
3.0
43 
1.0
30 
2.0
23 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row3.0
4th row2.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.0 51
34.7%
3.0 43
29.3%
1.0 30
20.4%
2.0 23
15.6%

Length

2023-07-15T16:11:47.998941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T16:11:48.264968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
4.0 51
34.7%
3.0 43
29.3%
1.0 30
20.4%
2.0 23
15.6%

Most occurring characters

ValueCountFrequency (%)
. 147
33.3%
0 147
33.3%
4 51
 
11.6%
3 43
 
9.8%
1 30
 
6.8%
2 23
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294
66.7%
Other Punctuation 147
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 147
50.0%
4 51
 
17.3%
3 43
 
14.6%
1 30
 
10.2%
2 23
 
7.8%
Other Punctuation
ValueCountFrequency (%)
. 147
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 147
33.3%
0 147
33.3%
4 51
 
11.6%
3 43
 
9.8%
1 30
 
6.8%
2 23
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 147
33.3%
0 147
33.3%
4 51
 
11.6%
3 43
 
9.8%
1 30
 
6.8%
2 23
 
5.2%

MaritalStatus
Categorical

Distinct3
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
Married
69 
Single
49 
Divorced
29 

Length

Max length8
Median length7
Mean length6.8639456
Min length6

Characters and Unicode

Total characters1009
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowSingle
3rd rowMarried
4th rowDivorced
5th rowSingle

Common Values

ValueCountFrequency (%)
Married 69
46.9%
Single 49
33.3%
Divorced 29
19.7%

Length

2023-07-15T16:11:48.528251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T16:11:48.811288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
married 69
46.9%
single 49
33.3%
divorced 29
19.7%

Most occurring characters

ValueCountFrequency (%)
r 167
16.6%
i 147
14.6%
e 147
14.6%
d 98
9.7%
M 69
6.8%
a 69
6.8%
S 49
 
4.9%
n 49
 
4.9%
g 49
 
4.9%
l 49
 
4.9%
Other values (4) 116
11.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 862
85.4%
Uppercase Letter 147
 
14.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 167
19.4%
i 147
17.1%
e 147
17.1%
d 98
11.4%
a 69
8.0%
n 49
 
5.7%
g 49
 
5.7%
l 49
 
5.7%
v 29
 
3.4%
o 29
 
3.4%
Uppercase Letter
ValueCountFrequency (%)
M 69
46.9%
S 49
33.3%
D 29
19.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 1009
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 167
16.6%
i 147
14.6%
e 147
14.6%
d 98
9.7%
M 69
6.8%
a 69
6.8%
S 49
 
4.9%
n 49
 
4.9%
g 49
 
4.9%
l 49
 
4.9%
Other values (4) 116
11.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 167
16.6%
i 147
14.6%
e 147
14.6%
d 98
9.7%
M 69
6.8%
a 69
6.8%
S 49
 
4.9%
n 49
 
4.9%
g 49
 
4.9%
l 49
 
4.9%
Other values (4) 116
11.5%

MonthlyIncome
Real number (ℝ)

Distinct146
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6914.1837
Minimum1102
Maximum19943
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-07-15T16:11:49.066036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1102
5-th percentile2143.5
Q13135.5
median5154
Q39625
95-th percentile17794.4
Maximum19943
Range18841
Interquartile range (IQR)6489.5

Descriptive statistics

Standard deviation4960.3142
Coefficient of variation (CV)0.71741141
Kurtosis0.33407357
Mean6914.1837
Median Absolute Deviation (MAD)2458
Skewness1.1871549
Sum1016385
Variance24604717
MonotonicityNot monotonic
2023-07-15T16:11:49.367542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2610 2
 
1.4%
2559 1
 
0.7%
2613 1
 
0.7%
6380 1
 
0.7%
5175 1
 
0.7%
6500 1
 
0.7%
10266 1
 
0.7%
2235 1
 
0.7%
6077 1
 
0.7%
8103 1
 
0.7%
Other values (136) 136
92.5%
ValueCountFrequency (%)
1102 1
0.7%
1261 1
0.7%
2011 1
0.7%
2073 1
0.7%
2090 1
0.7%
2096 1
0.7%
2097 1
0.7%
2133 1
0.7%
2168 1
0.7%
2231 1
0.7%
ValueCountFrequency (%)
19943 1
0.7%
19859 1
0.7%
19847 1
0.7%
18880 1
0.7%
18665 1
0.7%
18606 1
0.7%
18265 1
0.7%
17861 1
0.7%
17639 1
0.7%
17426 1
0.7%

MonthlyRate
Real number (ℝ)

Distinct147
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14563.449
Minimum2104
Maximum26933
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-07-15T16:11:49.659707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2104
5-th percentile2966.6
Q18889.5
median15146
Q320470.5
95-th percentile25234
Maximum26933
Range24829
Interquartile range (IQR)11581

Descriptive statistics

Standard deviation7003.8401
Coefficient of variation (CV)0.48091906
Kurtosis-1.1128955
Mean14563.449
Median Absolute Deviation (MAD)5905
Skewness-0.086662918
Sum2140827
Variance49053776
MonotonicityNot monotonic
2023-07-15T16:11:49.951860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17852 1
 
0.7%
12066 1
 
0.7%
9541 1
 
0.7%
6110 1
 
0.7%
22162 1
 
0.7%
13305 1
 
0.7%
2845 1
 
0.7%
6233 1
 
0.7%
14377 1
 
0.7%
14814 1
 
0.7%
Other values (137) 137
93.2%
ValueCountFrequency (%)
2104 1
0.7%
2288 1
0.7%
2338 1
0.7%
2396 1
0.7%
2561 1
0.7%
2689 1
0.7%
2845 1
0.7%
2939 1
0.7%
3031 1
0.7%
3458 1
0.7%
ValueCountFrequency (%)
26933 1
0.7%
26342 1
0.7%
26009 1
0.7%
25995 1
0.7%
25952 1
0.7%
25594 1
0.7%
25275 1
0.7%
25258 1
0.7%
25178 1
0.7%
24941 1
0.7%

NumCompaniesWorked
Real number (ℝ)

Distinct10
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8095238
Minimum0
Maximum9
Zeros15
Zeros (%)10.2%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-07-15T16:11:50.220288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.3852668
Coefficient of variation (CV)0.84899327
Kurtosis0.029483767
Mean2.8095238
Median Absolute Deviation (MAD)1
Skewness0.94437088
Sum413
Variance5.6894977
MonotonicityNot monotonic
2023-07-15T16:11:50.416921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 49
33.3%
4 18
 
12.2%
2 17
 
11.6%
3 16
 
10.9%
0 15
 
10.2%
5 10
 
6.8%
6 8
 
5.4%
8 6
 
4.1%
7 4
 
2.7%
9 4
 
2.7%
ValueCountFrequency (%)
0 15
 
10.2%
1 49
33.3%
2 17
 
11.6%
3 16
 
10.9%
4 18
 
12.2%
5 10
 
6.8%
6 8
 
5.4%
7 4
 
2.7%
8 6
 
4.1%
9 4
 
2.7%
ValueCountFrequency (%)
9 4
 
2.7%
8 6
 
4.1%
7 4
 
2.7%
6 8
 
5.4%
5 10
 
6.8%
4 18
 
12.2%
3 16
 
10.9%
2 17
 
11.6%
1 49
33.3%
0 15
 
10.2%

Over18
Boolean

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
True
147 
ValueCountFrequency (%)
True 147
100.0%
2023-07-15T16:11:50.659709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

OverTime
Boolean

Distinct2
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
False
97 
True
50 
ValueCountFrequency (%)
False 97
66.0%
True 50
34.0%
2023-07-15T16:11:50.871711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

PercentSalaryHike
Real number (ℝ)

Distinct15
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.877551
Minimum11
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-07-15T16:11:51.076230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q112.5
median15
Q319
95-th percentile24
Maximum25
Range14
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation4.1079575
Coefficient of variation (CV)0.2587274
Kurtosis-0.67117256
Mean15.877551
Median Absolute Deviation (MAD)3
Skewness0.70249529
Sum2334
Variance16.875315
MonotonicityNot monotonic
2023-07-15T16:11:51.283389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
12 21
14.3%
13 20
13.6%
11 16
10.9%
14 16
10.9%
18 11
7.5%
15 10
6.8%
16 9
 
6.1%
22 8
 
5.4%
19 7
 
4.8%
20 6
 
4.1%
Other values (5) 23
15.6%
ValueCountFrequency (%)
11 16
10.9%
12 21
14.3%
13 20
13.6%
14 16
10.9%
15 10
6.8%
16 9
6.1%
17 6
 
4.1%
18 11
7.5%
19 7
 
4.8%
20 6
 
4.1%
ValueCountFrequency (%)
25 4
 
2.7%
24 6
4.1%
23 4
 
2.7%
22 8
5.4%
21 3
 
2.0%
20 6
4.1%
19 7
4.8%
18 11
7.5%
17 6
4.1%
16 9
6.1%
Distinct2
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
3.0
116 
4.0
31 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 116
78.9%
4.0 31
 
21.1%

Length

2023-07-15T16:11:51.534713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T16:11:51.829535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0 116
78.9%
4.0 31
 
21.1%

Most occurring characters

ValueCountFrequency (%)
. 147
33.3%
0 147
33.3%
3 116
26.3%
4 31
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294
66.7%
Other Punctuation 147
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 147
50.0%
3 116
39.5%
4 31
 
10.5%
Other Punctuation
ValueCountFrequency (%)
. 147
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 147
33.3%
0 147
33.3%
3 116
26.3%
4 31
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 147
33.3%
0 147
33.3%
3 116
26.3%
4 31
 
7.0%
Distinct4
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
3.0
47 
4.0
45 
1.0
31 
2.0
24 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row2.0
3rd row3.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 47
32.0%
4.0 45
30.6%
1.0 31
21.1%
2.0 24
16.3%

Length

2023-07-15T16:11:52.083758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T16:11:52.364573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0 47
32.0%
4.0 45
30.6%
1.0 31
21.1%
2.0 24
16.3%

Most occurring characters

ValueCountFrequency (%)
. 147
33.3%
0 147
33.3%
3 47
 
10.7%
4 45
 
10.2%
1 31
 
7.0%
2 24
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294
66.7%
Other Punctuation 147
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 147
50.0%
3 47
 
16.0%
4 45
 
15.3%
1 31
 
10.5%
2 24
 
8.2%
Other Punctuation
ValueCountFrequency (%)
. 147
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 147
33.3%
0 147
33.3%
3 47
 
10.7%
4 45
 
10.2%
1 31
 
7.0%
2 24
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 147
33.3%
0 147
33.3%
3 47
 
10.7%
4 45
 
10.2%
1 31
 
7.0%
2 24
 
5.4%

StandardHours
Categorical

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
80.0
147 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters588
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row80.0
2nd row80.0
3rd row80.0
4th row80.0
5th row80.0

Common Values

ValueCountFrequency (%)
80.0 147
100.0%

Length

2023-07-15T16:11:52.628399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T16:11:52.968485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
80.0 147
100.0%

Most occurring characters

ValueCountFrequency (%)
0 294
50.0%
8 147
25.0%
. 147
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 441
75.0%
Other Punctuation 147
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 294
66.7%
8 147
33.3%
Other Punctuation
ValueCountFrequency (%)
. 147
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 588
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 294
50.0%
8 147
25.0%
. 147
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 588
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 294
50.0%
8 147
25.0%
. 147
25.0%

StockOptionLevel
Categorical

Distinct4
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
0.0
68 
1.0
57 
2.0
16 
3.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row2.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 68
46.3%
1.0 57
38.8%
2.0 16
 
10.9%
3.0 6
 
4.1%

Length

2023-07-15T16:11:53.340486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T16:11:53.809941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 68
46.3%
1.0 57
38.8%
2.0 16
 
10.9%
3.0 6
 
4.1%

Most occurring characters

ValueCountFrequency (%)
0 215
48.8%
. 147
33.3%
1 57
 
12.9%
2 16
 
3.6%
3 6
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294
66.7%
Other Punctuation 147
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 215
73.1%
1 57
 
19.4%
2 16
 
5.4%
3 6
 
2.0%
Other Punctuation
ValueCountFrequency (%)
. 147
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 215
48.8%
. 147
33.3%
1 57
 
12.9%
2 16
 
3.6%
3 6
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 215
48.8%
. 147
33.3%
1 57
 
12.9%
2 16
 
3.6%
3 6
 
1.4%

TotalWorkingYears
Real number (ℝ)

Distinct31
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.612245
Minimum1
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-07-15T16:11:54.247710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.3
Q16
median10
Q317
95-th percentile26
Maximum36
Range35
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.4571369
Coefficient of variation (CV)0.64217875
Kurtosis0.37093514
Mean11.612245
Median Absolute Deviation (MAD)5
Skewness0.91738102
Sum1707
Variance55.60889
MonotonicityNot monotonic
2023-07-15T16:11:54.707827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
10 22
15.0%
8 12
 
8.2%
4 12
 
8.2%
6 10
 
6.8%
9 10
 
6.8%
7 8
 
5.4%
17 7
 
4.8%
3 6
 
4.1%
16 6
 
4.1%
21 5
 
3.4%
Other values (21) 49
33.3%
ValueCountFrequency (%)
1 5
 
3.4%
2 3
 
2.0%
3 6
 
4.1%
4 12
8.2%
5 3
 
2.0%
6 10
6.8%
7 8
 
5.4%
8 12
8.2%
9 10
6.8%
10 22
15.0%
ValueCountFrequency (%)
36 1
 
0.7%
33 1
 
0.7%
31 1
 
0.7%
30 1
 
0.7%
28 2
1.4%
26 3
2.0%
25 1
 
0.7%
24 2
1.4%
23 1
 
0.7%
22 3
2.0%

TrainingTimesLastYear
Real number (ℝ)

Distinct7
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9727891
Minimum0
Maximum6
Zeros5
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-07-15T16:11:55.166967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3993422
Coefficient of variation (CV)0.47071694
Kurtosis0.03272992
Mean2.9727891
Median Absolute Deviation (MAD)1
Skewness0.52035578
Sum437
Variance1.9581586
MonotonicityNot monotonic
2023-07-15T16:11:55.563485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 50
34.0%
2 50
34.0%
5 17
 
11.6%
6 10
 
6.8%
4 9
 
6.1%
1 6
 
4.1%
0 5
 
3.4%
ValueCountFrequency (%)
0 5
 
3.4%
1 6
 
4.1%
2 50
34.0%
3 50
34.0%
4 9
 
6.1%
5 17
 
11.6%
6 10
 
6.8%
ValueCountFrequency (%)
6 10
 
6.8%
5 17
 
11.6%
4 9
 
6.1%
3 50
34.0%
2 50
34.0%
1 6
 
4.1%
0 5
 
3.4%

WorkLifeBalance
Categorical

Distinct4
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
3.0
82 
2.0
37 
4.0
17 
1.0
11 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row3.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.0 82
55.8%
2.0 37
25.2%
4.0 17
 
11.6%
1.0 11
 
7.5%

Length

2023-07-15T16:11:56.000001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T16:11:56.358668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0 82
55.8%
2.0 37
25.2%
4.0 17
 
11.6%
1.0 11
 
7.5%

Most occurring characters

ValueCountFrequency (%)
. 147
33.3%
0 147
33.3%
3 82
18.6%
2 37
 
8.4%
4 17
 
3.9%
1 11
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294
66.7%
Other Punctuation 147
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 147
50.0%
3 82
27.9%
2 37
 
12.6%
4 17
 
5.8%
1 11
 
3.7%
Other Punctuation
ValueCountFrequency (%)
. 147
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 147
33.3%
0 147
33.3%
3 82
18.6%
2 37
 
8.4%
4 17
 
3.9%
1 11
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 147
33.3%
0 147
33.3%
3 82
18.6%
2 37
 
8.4%
4 17
 
3.9%
1 11
 
2.5%

YearsAtCompany
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.829932
Minimum0
Maximum29
Zeros3
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-07-15T16:11:56.592491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q39
95-th percentile20
Maximum29
Range29
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.4148124
Coefficient of variation (CV)0.7928062
Kurtosis2.2715387
Mean6.829932
Median Absolute Deviation (MAD)2
Skewness1.5125811
Sum1004
Variance29.320194
MonotonicityNot monotonic
2023-07-15T16:11:56.830906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
5 22
15.0%
3 18
12.2%
7 13
8.8%
2 12
8.2%
4 12
8.2%
10 11
7.5%
1 11
7.5%
6 9
 
6.1%
8 9
 
6.1%
9 6
 
4.1%
Other values (12) 24
16.3%
ValueCountFrequency (%)
0 3
 
2.0%
1 11
7.5%
2 12
8.2%
3 18
12.2%
4 12
8.2%
5 22
15.0%
6 9
6.1%
7 13
8.8%
8 9
6.1%
9 6
 
4.1%
ValueCountFrequency (%)
29 1
 
0.7%
22 1
 
0.7%
21 2
 
1.4%
20 5
3.4%
19 1
 
0.7%
18 2
 
1.4%
16 2
 
1.4%
15 2
 
1.4%
14 2
 
1.4%
13 2
 
1.4%

YearsInCurrentRole
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.414966
Minimum0
Maximum16
Zeros15
Zeros (%)10.2%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-07-15T16:11:57.067717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile10.7
Maximum16
Range16
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.3878177
Coefficient of variation (CV)0.76734854
Kurtosis0.26713421
Mean4.414966
Median Absolute Deviation (MAD)2
Skewness0.81512377
Sum649
Variance11.477309
MonotonicityNot monotonic
2023-07-15T16:11:57.298917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2 40
27.2%
7 32
21.8%
3 15
 
10.2%
0 15
 
10.2%
4 9
 
6.1%
1 7
 
4.8%
5 6
 
4.1%
8 6
 
4.1%
9 4
 
2.7%
11 3
 
2.0%
Other values (6) 10
 
6.8%
ValueCountFrequency (%)
0 15
 
10.2%
1 7
 
4.8%
2 40
27.2%
3 15
 
10.2%
4 9
 
6.1%
5 6
 
4.1%
6 2
 
1.4%
7 32
21.8%
8 6
 
4.1%
9 4
 
2.7%
ValueCountFrequency (%)
16 1
 
0.7%
14 1
 
0.7%
13 2
 
1.4%
12 1
 
0.7%
11 3
 
2.0%
10 3
 
2.0%
9 4
 
2.7%
8 6
 
4.1%
7 32
21.8%
6 2
 
1.4%

YearsSinceLastPromotion
Real number (ℝ)

Distinct13
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0952381
Minimum0
Maximum15
Zeros56
Zeros (%)38.1%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-07-15T16:11:57.548856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32.5
95-th percentile8
Maximum15
Range15
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation2.9778482
Coefficient of variation (CV)1.4212457
Kurtosis3.6894507
Mean2.0952381
Median Absolute Deviation (MAD)1
Skewness1.9416844
Sum308
Variance8.8675799
MonotonicityNot monotonic
2023-07-15T16:11:57.768214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 56
38.1%
1 37
25.2%
2 17
 
11.6%
7 10
 
6.8%
3 7
 
4.8%
4 5
 
3.4%
5 4
 
2.7%
11 3
 
2.0%
8 3
 
2.0%
6 2
 
1.4%
Other values (3) 3
 
2.0%
ValueCountFrequency (%)
0 56
38.1%
1 37
25.2%
2 17
 
11.6%
3 7
 
4.8%
4 5
 
3.4%
5 4
 
2.7%
6 2
 
1.4%
7 10
 
6.8%
8 3
 
2.0%
9 1
 
0.7%
ValueCountFrequency (%)
15 1
 
0.7%
13 1
 
0.7%
11 3
 
2.0%
9 1
 
0.7%
8 3
 
2.0%
7 10
6.8%
6 2
 
1.4%
5 4
 
2.7%
4 5
3.4%
3 7
4.8%

YearsWithCurrManager
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1292517
Minimum0
Maximum14
Zeros21
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-07-15T16:11:57.991033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile10
Maximum14
Range14
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.3109865
Coefficient of variation (CV)0.80183693
Kurtosis-0.48346777
Mean4.1292517
Median Absolute Deviation (MAD)2
Skewness0.62978518
Sum607
Variance10.962632
MonotonicityNot monotonic
2023-07-15T16:11:58.208385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2 40
27.2%
7 30
20.4%
0 21
14.3%
3 11
 
7.5%
4 10
 
6.8%
1 8
 
5.4%
8 7
 
4.8%
9 6
 
4.1%
5 5
 
3.4%
10 4
 
2.7%
Other values (4) 5
 
3.4%
ValueCountFrequency (%)
0 21
14.3%
1 8
 
5.4%
2 40
27.2%
3 11
 
7.5%
4 10
 
6.8%
5 5
 
3.4%
7 30
20.4%
8 7
 
4.8%
9 6
 
4.1%
10 4
 
2.7%
ValueCountFrequency (%)
14 1
 
0.7%
13 1
 
0.7%
12 1
 
0.7%
11 2
 
1.4%
10 4
 
2.7%
9 6
 
4.1%
8 7
 
4.8%
7 30
20.4%
5 5
 
3.4%
4 10
 
6.8%

Attrition
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing147
Missing (%)100.0%
Memory size2.3 KiB

Interactions

2023-07-15T16:11:32.999855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:34.593750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:38.128273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:41.564617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:47.333527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:52.187663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:55.686270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:01.960639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:05.459460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:08.743438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:12.477476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:16.998787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:20.535203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:23.964750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:29.558577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:33.234082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:34.848808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:38.365720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:41.825001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:48.305306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:52.426571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:55.933652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:02.195121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:05.700857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:08.975260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:12.841511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:17.242565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:20.783505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:24.221475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:29.791574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:33.459582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:35.061221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:38.562216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:42.024764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:48.513099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:52.626986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:56.156194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:02.414588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:05.890210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:09.179576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:13.197617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:17.458195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:20.984561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:24.443022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:29.998950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:33.699712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:35.288648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:38.787937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:42.234679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:48.790775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:52.847916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:56.415292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:02.653740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:06.100269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:09.407511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:13.588773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:17.690228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:21.216517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:24.688724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:30.236622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:33.932824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:35.542152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:39.001703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:42.490785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:49.373402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:53.074737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:56.646354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:02.906361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:06.319315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:09.627424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:13.986421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:17.921233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:21.447836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:24.926052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:30.474466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:34.179531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:35.777603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:39.252630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:42.755564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:49.810092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:53.318291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:56.895333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:03.126534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:06.541045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:09.876809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:14.302086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:18.161958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:21.684239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:25.171132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:30.712251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:34.423360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:36.012893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:39.495479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:42.995548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:50.053766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:53.552770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:57.134161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:03.377536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:06.781409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:10.105415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:14.688297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:18.400073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:21.913019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:25.446137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:30.937496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:34.655585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:36.222469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:39.714638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:43.204297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:50.274029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:53.755612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:57.383956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:03.575027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:06.980889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:10.316705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:15.017978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:18.613368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:22.127480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:25.813472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:31.149385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:34.867270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:36.457525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:39.914706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:43.461370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:50.499288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:53.953499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:57.601439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:03.775358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:07.170485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:10.523045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:15.358757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:18.813641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:22.341560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:26.159873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:31.358131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:35.094167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:36.697906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:40.143592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:43.709454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:50.741195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:54.210654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:57.835620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:04.021318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:07.383960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:10.763510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:15.590404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:19.047396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:22.567651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:26.530968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:31.596194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:35.335530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:36.934645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:40.383751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:43.948014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:50.989532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:54.454351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:00.443188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:04.255487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:07.618923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:10.986217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:15.819511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:19.291146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:22.794516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:26.857109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:31.826432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:35.570991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:37.163732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:40.616526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:44.209601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:51.244040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:54.682662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:00.735615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:04.511868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:07.838998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:11.207965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:16.051408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:19.538480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:23.020534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:27.226772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:32.044812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:35.810823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:37.402634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:40.872394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:44.538150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:51.471408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:54.906749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:01.080287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:04.751848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:08.058788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:11.443717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:16.280492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:19.799505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:23.254351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:27.600188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:32.267595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:36.065186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:37.654835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:41.113537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:44.962155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:51.720922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:55.161136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:01.485722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:05.007366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:08.283030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:11.782681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:16.520098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:20.062522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:23.505103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:29.059855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:32.515131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:36.300911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:37.894812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:41.339300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:46.533648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:51.947175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:10:55.429491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:01.728325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:05.228287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:08.514103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:12.131687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:16.762867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:20.292613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:23.736832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:29.304328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:11:32.751328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-15T16:11:58.489020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
EmployeeNumberAgeDailyRateDistanceFromHomeHourlyRateMonthlyIncomeMonthlyRateNumCompaniesWorkedPercentSalaryHikeTotalWorkingYearsTrainingTimesLastYearYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManagerBusinessTravelDepartmentEducationEducationFieldEnvironmentSatisfactionGenderJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusOverTimePerformanceRatingRelationshipSatisfactionStockOptionLevelWorkLifeBalance
EmployeeNumber1.0000.004-0.1370.063-0.0170.110-0.0600.0070.0980.0680.1270.1060.0750.0370.0720.1820.0000.0000.1140.0570.0000.1510.0600.0870.0000.0000.2700.0000.1860.0000.000
Age0.0041.000-0.0610.0240.1110.485-0.0550.348-0.0130.7050.0870.2800.2160.1090.1750.0000.0360.1060.0000.1070.1420.0000.2940.0370.0000.2080.0000.0000.0000.0000.000
DailyRate-0.137-0.0611.0000.0450.017-0.033-0.0560.130-0.035-0.045-0.081-0.0100.009-0.2240.0100.1090.0000.0000.0000.0920.0000.0000.0570.0000.1100.1710.1690.1600.0510.1460.000
DistanceFromHome0.0630.0240.0451.0000.1150.0160.079-0.0880.077-0.0090.0400.0880.101-0.0350.0760.0000.0000.1310.1230.0000.0000.0000.0000.0000.1180.0620.0000.0000.0000.0000.084
HourlyRate-0.0170.1110.0170.1151.0000.1120.0330.1920.0660.148-0.0860.0300.0640.1430.0200.0000.1110.1040.0000.0000.1240.0000.0000.0510.0960.0000.0000.0000.0000.0000.100
MonthlyIncome0.1100.485-0.0330.0160.1121.000-0.1220.274-0.0520.670-0.0200.3630.2730.1150.2700.0000.2890.3210.0470.1800.0000.0000.8500.4130.0520.0000.1090.0000.0000.0000.000
MonthlyRate-0.060-0.055-0.0560.0790.033-0.1221.000-0.085-0.129-0.0740.1230.0650.0600.0360.0490.0410.0000.0000.0360.0000.0430.0470.1930.1430.0000.1900.0000.0000.0000.0000.173
NumCompaniesWorked0.0070.3480.130-0.0880.1920.274-0.0851.0000.0110.358-0.136-0.151-0.145-0.128-0.1420.0000.0000.0740.0000.0000.0000.0660.1670.1850.0000.0000.0000.0000.0000.0000.000
PercentSalaryHike0.098-0.013-0.0350.0770.066-0.052-0.1290.0111.000-0.0040.038-0.096-0.092-0.032-0.0430.1410.0000.1220.0000.0000.1960.1390.0000.0000.0720.0200.0230.9720.1180.0800.000
TotalWorkingYears0.0680.705-0.045-0.0090.1480.670-0.0740.358-0.0041.0000.0530.5670.4930.1550.4450.0000.0860.1430.0290.0000.0800.0000.5440.2730.1340.1810.1670.0000.0460.0270.000
TrainingTimesLastYear0.1270.087-0.0810.040-0.086-0.0200.123-0.1360.0380.0531.0000.019-0.0060.074-0.0110.0760.0000.0800.0560.0000.0000.0170.0250.1180.0300.1230.1900.0000.0120.0000.000
YearsAtCompany0.1060.280-0.0100.0880.0300.3630.065-0.151-0.0960.5670.0191.0000.9020.3510.8230.0000.1110.0000.0000.1190.0000.0000.3230.2070.0430.1890.0000.0370.1160.0000.097
YearsInCurrentRole0.0750.2160.0090.1010.0640.2730.060-0.145-0.0920.493-0.0060.9021.0000.4150.7710.1290.0000.0000.0000.0280.2210.1140.1690.0720.0000.2350.0000.0000.0000.1050.000
YearsSinceLastPromotion0.0370.109-0.224-0.0350.1430.1150.036-0.128-0.0320.1550.0740.3510.4151.0000.3360.0430.1100.0000.0000.0000.0000.0000.2960.1770.0560.1530.1420.0950.0000.0000.000
YearsWithCurrManager0.0720.1750.0100.0760.0200.2700.049-0.142-0.0430.445-0.0110.8230.7710.3361.0000.1140.0880.0000.0740.0890.0000.0000.1920.0680.0000.1700.0000.2150.0000.0940.000
BusinessTravel0.1820.0000.1090.0000.0000.0000.0410.0000.1410.0000.0760.0000.1290.0430.1141.0000.1000.0640.0720.0000.0760.0000.0000.0000.0000.0760.0830.0000.0000.0460.000
Department0.0000.0360.0000.0000.1110.2890.0000.0000.0000.0860.0000.1110.0000.1100.0880.1001.0000.1480.5380.1210.0000.0000.2910.8850.0000.0850.0000.0000.0410.1610.152
Education0.0000.1060.0000.1310.1040.3210.0000.0740.1220.1430.0800.0000.0000.0000.0000.0640.1481.0000.0810.0000.0600.0000.1650.1360.1170.0000.1490.0930.0370.0000.000
EducationField0.1140.0000.0000.1230.0000.0470.0360.0000.0000.0290.0560.0000.0000.0000.0740.0720.5380.0811.0000.0980.1150.0140.0410.2830.0000.0000.0000.0000.0270.0000.000
EnvironmentSatisfaction0.0570.1070.0920.0000.0000.1800.0000.0000.0000.0000.0000.1190.0280.0000.0890.0000.1210.0000.0981.0000.0000.0000.1130.0000.1560.1150.1030.0000.0910.0000.000
Gender0.0000.1420.0000.0000.1240.0000.0430.0000.1960.0800.0000.0000.2210.0000.0000.0760.0000.0600.1150.0001.0000.0000.0000.0000.1830.1380.0000.0000.0000.0000.000
JobInvolvement0.1510.0000.0000.0000.0000.0000.0470.0660.1390.0000.0170.0000.1140.0000.0000.0000.0000.0000.0140.0000.0001.0000.0000.0820.0150.2080.0000.0000.0000.2130.043
JobLevel0.0600.2940.0570.0000.0000.8500.1930.1670.0000.5440.0250.3230.1690.2960.1920.0000.2910.1650.0410.1130.0000.0001.0000.5590.0000.1100.1530.0000.0000.0000.094
JobRole0.0870.0370.0000.0000.0510.4130.1430.1850.0000.2730.1180.2070.0720.1770.0680.0000.8850.1360.2830.0000.0000.0820.5591.0000.0000.1310.0000.1240.0000.1580.188
JobSatisfaction0.0000.0000.1100.1180.0960.0520.0000.0000.0720.1340.0300.0430.0000.0560.0000.0000.0000.1170.0000.1560.1830.0150.0000.0001.0000.0730.0000.0870.0000.0000.000
MaritalStatus0.0000.2080.1710.0620.0000.0000.1900.0000.0200.1810.1230.1890.2350.1530.1700.0760.0850.0000.0000.1150.1380.2080.1100.1310.0731.0000.1160.0000.0000.5750.000
OverTime0.2700.0000.1690.0000.0000.1090.0000.0000.0230.1670.1900.0000.0000.1420.0000.0830.0000.1490.0000.1030.0000.0000.1530.0000.0000.1161.0000.0000.0000.0250.156
PerformanceRating0.0000.0000.1600.0000.0000.0000.0000.0000.9720.0000.0000.0370.0000.0950.2150.0000.0000.0930.0000.0000.0000.0000.0000.1240.0870.0000.0001.0000.1440.0810.000
RelationshipSatisfaction0.1860.0000.0510.0000.0000.0000.0000.0000.1180.0460.0120.1160.0000.0000.0000.0000.0410.0370.0270.0910.0000.0000.0000.0000.0000.0000.0000.1441.0000.0000.000
StockOptionLevel0.0000.0000.1460.0000.0000.0000.0000.0000.0800.0270.0000.0000.1050.0000.0940.0460.1610.0000.0000.0000.0000.2130.0000.1580.0000.5750.0250.0810.0001.0000.123
WorkLifeBalance0.0000.0000.0000.0840.1000.0000.1730.0000.0000.0000.0000.0970.0000.0000.0000.0000.1520.0000.0000.0000.0000.0430.0940.1880.0000.0000.1560.0000.0000.1231.000

Missing values

2023-07-15T16:11:36.749692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-15T16:11:37.688332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

EmployeeNumberAgeBusinessTravelDailyRateDepartmentDistanceFromHomeEducationEducationFieldEmployeeCountEnvironmentSatisfactionGenderHourlyRateJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeMonthlyRateNumCompaniesWorkedOver18OverTimePercentSalaryHikePerformanceRatingRelationshipSatisfactionStandardHoursStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManagerAttrition
132310000035.0Travel_Rarely1343.0Research & Development27.01.0Medical1.03.0Female53.02.01.0Research Scientist1.0Single2559.017852.01.0YNo11.03.04.080.00.06.03.02.06.05.01.01.0NaN
132410000127.0Travel_Rarely1220.0Research & Development5.03.0Life Sciences1.03.0Female85.03.01.0Research Scientist2.0Single2478.020938.01.0YYes12.03.02.080.00.04.02.02.04.03.01.02.0NaN
132510000224.0Travel_Rarely1476.0Sales4.01.0Medical1.04.0Female42.03.02.0Sales Executive3.0Married4162.015211.01.0YYes12.03.03.080.02.05.03.03.05.04.00.03.0NaN
132610000337.0Non-Travel142.0Sales9.04.0Medical1.01.0Male69.03.03.0Sales Executive2.0Divorced8834.024666.01.0YNo13.03.04.080.01.09.06.03.09.05.07.07.0NaN
132710000433.0Travel_Rarely527.0Research & Development1.04.0Other1.04.0Male63.03.01.0Research Scientist4.0Single2686.05207.01.0YYes13.03.03.080.00.010.02.02.010.09.07.08.0NaN
132810000538.0Travel_Rarely723.0Sales2.04.0Marketing1.02.0Female77.01.02.0Sales Representative4.0Married5405.04244.02.0YYes20.04.01.080.02.020.04.02.04.02.00.03.0NaN
132910000639.0Travel_Frequently766.0Sales20.03.0Life Sciences1.03.0Male83.03.02.0Sales Executive4.0Divorced4127.019188.02.0YNo18.03.04.080.01.07.06.03.02.01.02.02.0NaN
133010000730.0Travel_Rarely1358.0Sales16.01.0Life Sciences1.04.0Male96.03.02.0Sales Executive3.0Married5301.02939.08.0YNo15.03.03.080.02.04.02.02.02.01.02.02.0NaN
133110000835.0Travel_Rarely1370.0Research & Development27.04.0Life Sciences1.04.0Male49.03.02.0Manufacturing Director3.0Married6883.05151.02.0YNo16.03.02.080.01.017.03.03.07.07.00.07.0NaN
133210000956.0Travel_Rarely718.0Research & Development4.04.0Technical Degree1.04.0Female92.03.05.0Manager1.0Divorced19943.018575.04.0YNo13.03.04.080.01.028.02.03.05.02.04.02.0NaN
EmployeeNumberAgeBusinessTravelDailyRateDepartmentDistanceFromHomeEducationEducationFieldEmployeeCountEnvironmentSatisfactionGenderHourlyRateJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeMonthlyRateNumCompaniesWorkedOver18OverTimePercentSalaryHikePerformanceRatingRelationshipSatisfactionStandardHoursStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManagerAttrition
146010013735.0Travel_Rarely538.0Research & Development25.02.0Other1.01.0Male54.02.02.0Laboratory Technician4.0Single3681.014004.04.0YNo14.03.04.080.00.09.03.03.03.02.00.02.0NaN
146110013829.0Travel_Rarely1210.0Sales2.03.0Medical1.01.0Male78.02.02.0Sales Executive2.0Married6644.03687.02.0YNo19.03.02.080.02.010.02.03.00.00.00.00.0NaN
146210013925.0Travel_Rarely266.0Research & Development1.03.0Medical1.04.0Female40.03.01.0Research Scientist2.0Single2096.018830.01.0YNo18.03.04.080.00.02.03.02.02.02.02.01.0NaN
146310014055.0Travel_Rarely725.0Research & Development2.03.0Medical1.04.0Male78.03.05.0Manager1.0Married19859.021199.05.0YYes13.03.04.080.01.024.02.03.05.02.01.04.0NaN
146410014137.0Travel_Rarely921.0Research & Development10.03.0Medical1.03.0Female98.03.01.0Laboratory Technician1.0Married3452.017663.06.0YNo20.04.02.080.01.017.03.03.05.04.00.03.0NaN
146510014235.0Non-Travel208.0Research & Development8.04.0Life Sciences1.03.0Female52.03.02.0Healthcare Representative3.0Married4148.012250.01.0YNo12.03.04.080.01.015.05.03.014.011.02.09.0NaN
146610014341.0Travel_Rarely582.0Research & Development28.04.0Life Sciences1.01.0Female60.02.04.0Manufacturing Director2.0Married13570.05640.00.0YNo23.04.03.080.01.021.03.03.020.07.00.010.0NaN
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